Deep learning-based forecasting of aggregated CSP production

This paper introduces deep learning-based forecasting models for the continuous prediction of the aggregated production generated by CSP plants in Spain. These models use as inputs the expected top of atmosphere irradiance values and available weather conditions forecasts for the locations where the main CSP power plants are installed. The performances of the forecast models are analysed and compared by means of the most extended metrics in the literature for a whole year of CSP energy production

Energy Forecasting in Smart Grid Systems: A Review of the State-of-the-art Techniques

Energy forecasting has a vital role to play in smart grid (SG) systems involving various applications such as demand-side management, load shedding, and optimum dispatch. Managing efficient forecasting while ensuring the least possible prediction error is one of the main challenges posed in the grid today, considering the uncertainty and granularity in SG data. This paper presents a comprehensive and application-oriented review of state-of-the-art forecasting methods for SG systems along with recent developments in probabilistic deep learning (PDL) considering different models and architectures. Traditional point forecasting methods including statistical, machine learning (ML), and deep learning (DL) are extensively investigated in terms of their applicability to energy forecasting. In addition, the significance of hybrid and data pre-processing techniques to support forecasting performance is also studied. A comparative case study using the Victorian electricity consumption and American electric power (AEP) datasets is conducted to analyze the performance of point and probabilistic forecasting methods. The analysis demonstrates higher accuracy of the long-short term memory (LSTM) models with appropriate hyper-parameter tuning among point forecasting methods especially when sample sizes are larger and involve nonlinear patterns with long sequences. Furthermore, Bayesian bidirectional LSTM (BLSTM) as a probabilistic method exhibit the highest accuracy in terms of least pinball score and root mean square error (RMSE)

Near real-time global solar radiation forecasting at multiple time-step horizons using the long short-term memory network

This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model, the partial autocorrelation function is applied to the high resolution, 1 min scaled solar radiation dataset that generates statistically significant lagged predictor variables describing the antecedent behaviour of GSR. The LSTM algorithm is adopted to capture the short- and the long-term dependencies within the GSR data series patterns to accurately predict the future GSR at 1, 5, 10, 15, and 30 min forecasting horizons. This objective model is benchmarked at a solar energy resource rich study site (Bac-Ninh, Vietnam) against the competing counterpart methods employing other deep learning, a statistical model, a single hidden layer and a machine learning-based model. The LSTM model generates satisfactory predictions at multiple-time step horizons, achieving a correlation coefficient exceeding 0.90, outperforming all of the counterparts. In accordance with robust statistical metrics and visual analysis of all tested data, the study ascertains the practicality of the proposed LSTM approach to generate reliable GSR forecasts. The Diebold–Mariano statistic test also shows LSTM outperforms the counterparts in most cases. The study confirms the practical utility of LSTM in renewable energy studies, and broadly in energy-monitoring devices tailored for other energy variables (e.g., hydro and wind energy)

Metodologia para a otimização de microrrede conectada ao sistema elétrico de distribuição utilizando a abordagem de controle preditivo baseado em modelo

Orientador: Prof. Dr. Odilon Luís TortelliCoorientador: Dr. Filipe PerezDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 21/02/2022Inclui referênciasÁrea de concentração: Sistemas de EnergiaResumo: Considerando a operação do sistema elétrico de potência moderno, muitos desafios têm surgido para permitir uma boa integração dos recursos renováveis, além da melhoria da qualidade da energia do sistema de distribuição e sua eficiência. Neste contexto, as microrredes conectadas surgem como uma solução robusta permitindo o gerenciamento ótimo da energia do sistema, melhorando índices de qualidade e trazendo benefícios operativos para rede. Os principais problemas de operação relatados em sistemas de distribuição estão diretamente relacionados com a grande penetração de fontes renováveis, que possuem característica intermitente, além dos picos de demanda de carga, que ocorre principalmente no horário de ponta, resultando em maiores perdas durante a operação do sistema e os problemas de tensão, que trazem impactos nos índices de qualidade da rede elétrica. Assim, este trabalho apresenta um estudo para a aplicação de controle preditivo baseado em modelo (Model Predictive Control - MPC) para a operação otimizada de uma microrrede conectada à rede elétrica de distribuição. A microrrede considerada para esse estudo é composta por um conjunto de cargas alimentadas por uma rede de distribuição de média tensão, um sistema de geração fotovoltaico de 200 kWp e um sistema de armazenamento de energia com baterias íon-lítio com capacidade total de 560 kWh. Tanto o sistema fotovoltaico quanto o sistema de armazenamento estão conectados na saída da subestação que abastece a cidade onde a microrrede está implementada. Para orientar o processo de implementação dos algoritmos de controle são considerados três modos de operação da microrrede, sendo eles a Redução do Pico de Demanda, a Suavização da Geração Fotovoltaica e Regulação de Tensão. Dessa forma, são formulados problemas de otimização para cada uma das operações da microrrede utilizando programação linear inteira mista (Mixed Integer Linear Programming - MILP). O controle preditivo determina os níveis de potência de carga ou descarga do sistema de armazenamento com base na minimização da função objetivo. Dessa forma, considerando a estratégia de horizonte deslizante, o controle MPC realiza um novo cálculo de otimização a cada dez minutos, ou seja, a cada intervalo de tempo são determinadas novas ações de controle com base nos valores atuais e previstos de geração fotovoltaica e demanda da carga da microrrede. A partir dos resultados obtidos com o controle preditivo são realizadas simulações computacionais utilizando o software GridLab-D que avaliam o impacto da operação de Redução de Pico, Suavização da Geração e Regulação de Tensão em um sistema teste sendo possível observar as melhorias em relação à redução do pico de demanda, flutuações de potência e magnitude de tensão no sistema com a aplicação da metodologia de controle preditivo desenvolvida.Abstract: Considering the operation of the modern electric power system, many challenges have arisen to allow a good integration of renewable resources, in addition to improving the energy quality and efficiency of the distribution system. In this context, connected microgrids emerge as a robust solution allowing optimal management of the power system, improving quality indices and bringing operational benefits to the grid. The main operating problems reported in distribution systems are directly related to the high penetration of renewable sources, which have an intermittent characteristic, in addition to peak load demand, which occurs mainly at peak hours, resulting in greater losses during the operation of the system and voltage problems, which impact the quality indices of the grid. The present work presents a study for the application of model-based predictive control for the optimized operation of a microgrid connected to the medium voltage distribution grid. The microgrid considered for this study is consist of a set of loads, a 200 kWp photovoltaic generation system and a 560 kWh Lithium-ion battery energy storage. Both the photovoltaic system and the storage system are connected at the substation that supplies the city where the microgrid is implemented. To guide the process of implementation of control algorithms are considered three modes of operation of the microgrid: Peak Shaving, Photovoltaic Generation Smoothing and Voltage Regulation. The optimization problems are formulated for each of the microgrid operations using mixed integer linear programming method. Predictive control determines the charge or discharge power levels of the storage system based on objective function minimization. Thus, considering the rolling horizon strategy, the MPC control performs a new optimization calculation every ten minutes, that is, at each time interval, new control actions are determined based on the current and predicted values of photovoltaic generation and load demand of the microgrid. From the results obtained with predictive control, computer simulations were performed using GridLab-D software to evaluate the impact of Peak Shaving, Generation Smoothing and Voltage Regulation on the test feeder being possible to observe the coherence of results and the improvements in the system with the application of the predictive control methodology developed